Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Scan integration as a labelling problem

Song, Ran, Liu, Yonghuai, Martin, Ralph Robert and Rosin, Paul L. 2014. Scan integration as a labelling problem. Pattern Recognition 47 (8) , pp. 2768-2782. 10.1016/j.patcog.2014.02.008

[img]
Preview
PDF - Accepted Post-Print Version
Download (1MB) | Preview

Abstract

Integration is a crucial step in the reconstruction of complete 3D surface model from multiple scans. Ever-present registration errors and scanning noise make integration a nontrivial problem. In this paper, we propose a novel method for multi-view scan integration where we solve it as a labelling problem. Unlike previous methods, which have been based on various merging schemes, our labelling-based method is essentially a selection strategy. The overall surface model is composed of surface patches from selected input scans. We formulate the labelling via a higher-order Markov Random Field (MRF) which assigns a label representing an index of some input scan to every point in a base surface. Using a higherorder MRF allows us to more effectively capture spatial relations between 3D points. We employ belief propagation to infer this labelling and experimentally demonstrate that this integration approach provides significantly improved integration via both qualitative and quantitative comparisons.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Additional Information: Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/0031-3203/(accessed 23/10/14). NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, [VOL 47, ISSUE 8, 2014] DOI 10.1016/j.patcog.2014.02.008
Publisher: Elsevier
ISSN: 0031-3203
Date of First Compliant Deposit: 30 March 2016
Last Modified: 28 Jun 2019 07:32
URI: http://orca.cf.ac.uk/id/eprint/60553

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics